An optimized object-based analysis for vegetation mapping using integration of Quickbird and Sentinel-1 data

This study proposed a workflow for an optimized object-based analysis for vegetation mapping using integration of Quickbird and Sentinel-1 data. The method is validated on a set of data captured over a part of Selangor located in the Peninsular Malaysia. The method comprised four components including image segmentation, Taguchi optimization, attribute selection using random forest, and rule-based feature extraction. Results indicated the robustness of the proposed approach as the area under curve of forest; grassland, old oil palm, rubber, urban tree, and young oil palm were calculated as 0.90, 0.89, 0.87, 0.87, 0.80, and 0.77, respectively. In addition, results showed that SAR data is very useful for extracting rubber and young oil palm trees (given by random forest importance values). Finally, further research is suggested to improve segmentation results and extract more features from the scene.     

Analytic hierarchy process and information value method-based landslide susceptibility mapping and vehicle vulnerability assessment along a highway in Sikkim Himalaya

In hilly areas, highway projects can be a cause of landslides as well as an element of vulnerability due to landslides. Hence, landslide susceptibility mapping of highway corridors can substantially mitigate loss of life and property. For this, a Landslide Susceptibility Assessment Model (LSAM) was developed for a corridor of 27 km along NH 10 in the East Sikkim. Landslide inducing factors viz. Aspect, Distance from Fault, Distance from Road, Drainage Density, Land use and Land cover, Lithology, Plan Curvature, Rainfall, Slope, Soil Depth, and Soil Texture were considered for the study. Results show that areas in proximity to the highway and areas with steeper slope had a higher landslide susceptibility than otherwise. Spatial explicit sensitivity analysis indicated that LSAM was sensitive to distance from the highway and slope. Vehicle vulnerability assessment of base year and horizon years showed that vulnerability increased through time. LSAM is appropriate for hazard mitigation for areas with poor historical data on landslides.     

Flood susceptibility mapping using integrated bivariate and multivariate statistical models

Flooding can have catastrophic effects on human lives and livelihoods and thus comprehensive flood management is needed. Such management requires information on the hydrologic, geotechnical, environmental, social, and economic aspects of flooding. The number of flood events that took place in Busan, South Korea, in 2009 exceeded the normal situation for that city. Mapping the susceptible areas helps us to understand flood trends and can aid in appropriate planning and flood prevention. In this study, a combination of bivariate probability analysis and multivariate logistic regression was used to produce flood susceptibility maps of Busan City. The main aim of this research was to overcome the weakness of logistic regression regarding bivariate probability capabilities. A flood inventory map with a total of 160 flood locations was extracted from various sources. Then, the flood inventory was randomly split into a testing dataset 70 % for training the models and the remaining 30 %, which was used for validation. Independent variables datasets included the rainfall, digital elevation model, slope, curvature, geology, green farmland, rivers, slope, soil drainage, soil effect, soil texture, stream power index, timber age, timber density, timber diameter, and timber type. The impact of each independent variable on flooding was evaluated by analyzing each independent variable with the dependent flood layer. The validation dataset, which was not used for model generation, was used to evaluate the flood susceptibility map using the prediction rate method. The results of the accuracy assessment showed a success rate of 92.7 % and a prediction rate of 82.3 %.     

Sediment yield assessment at basin scale using geospatial technique

Soil erosion and sediment yield from catchments are key limitations to achieving sustainable land use and maintaining water quality in nature. One of the important aspects in protecting the watershed is evaluation of sediment produced by statistical methods. Controlling sediment loading in protecting the watershed requires knowledge of soil erosion and sedimentation. Sediment yield is usually not available as a direct measurement but is estimated using geospatial models. One of the geospatial models for estimating sediment yield at the basin scale is sediment delivery ratio (SDR). The present study investigates the spatial SDR model in determining the sediment yield rate considering climate and physical factors of basin in geographic information system environment. This new approach was developed and tested on the Amammeh catchments in Iran. The validation of the model was evaluated using the Nash Sutcliffe efficiency coefficient. The developed model is not only conceptually easy and well suited to the local data needs but also requires less parameter, which offers less uncertainty in its application while meeting the intended purpose. The model is developed based on local data. The results predict strong variations in SDR from 0 in to 70 % in the uplands of the Basin.     

Debris flow impact assessment caused by 14 April 2012 rainfall along the Al-Hada Highway,Kingdom of Saudi Arabia using high-resolution satellite imagery

The Al-Hada highway that descends towards the west of the city of At-Taif is a major connecting highway in the western part of the Kingdom of Saudi Arabia. It is one of the series of descending roads connecting the holy city of Makkah and the city of Jeddah with the city of At-Taif and the cities farther south along the escarpment. The length of the Al-Hada highways is about 22 km. The Al-Hada highway has been historically exposed to landslides and other geohazards since the day it was opened to public some 60 years ago. The road has been reconstructed and many slope instabilities have been remediated and the road has been expanded to two lanes in each direction. Heavy rainfalls occurred on the 14th of April 2012 in the province of At-Taif, causing huge debris flows in two places along the Al-Hada highway. As a result of that, these debris flows closed all four lanes of the highway for 2 weeks in order to remove the debris. The current research deals with mapping of all debris flows along Al-Hada highway and determining their volumes and their impact on the road. Finally, suitable solutions have been suggested to address these critical sites to minimize and/or avoid the debris flow hazards in the future.     

Application of frequency ratio,statistical index,and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya

The Mugling–Narayanghat road section falls within the Lesser Himalaya and Siwalik zones of Central Nepal Himalaya and is highly deformed by the presence of numerous faults and folds. Over the years, this road section and its surrounding area have experienced repeated landslide activities. For that reason, landslide susceptibility zonation is essential for roadside slope disaster management and for planning further development activities. The main goal of this study was to investigate the application of the frequency ratio (FR), statistical index (SI), and weights-of-evidence (WoE) approaches for landslide susceptibility mapping of this road section and its surrounding area. For this purpose, the input layers of the landslide conditioning factors were prepared in the first stage. A landslide inventory map was prepared using earlier reports, aerial photographs interpretation, and multiple field surveys. A total of 438 landslide locations were detected. Out these, 295 (67 %) landslides were randomly selected as training data for the modeling using FR, SI, and WoE models and the remaining 143 (33 %) were used for the validation purposes. The landslide conditioning factors considered for the study area are slope gradient, slope aspect, plan curvature, altitude, stream power index, topographic wetness index, lithology, land use, distance from faults, distance from rivers, and distance from highway. The results were validated using area under the curve (AUC) analysis. From the analysis, it is seen that the FR model with a success rate of 76.8 % and predictive accuracy of 75.4 % performs better than WoE (success rate, 75.6 %; predictive accuracy, 74.9 %) and SI (success rate, 75.5 %; predictive accuracy, 74.6 %) models. Overall, all the models showed almost similar results. The resultant susceptibility maps can be useful for general land use planning.     

Coupling of DEM and remote-sensing-based approaches for semi-automated detection of regional geostructural features in Zagros mountain, Iran

In recent years, remote-sensing data have increasingly been used for the interpretation of objects and mapping in various applications of engineering geology. Digital elevation model (DEM) is very useful for detection, delineation, and interpretation of geological and structural features. The use of image elements for interpretation is a common method to extract structural features. In this paper, linear features were extracted from the Landsat ETM satellite image and then DEM was used to enhance those objects using digital-image-processing filtering techniques. The extraction procedures of the linear objects are performed in a semi-automated way. Photographic elements and geotechnical elements are used as main keys to extract the information from the satellite image data. This paper emphasizes on the application of DEM and usage of various filtering techniques with different convolution kernel size applied on the DEM. Additionally, this paper discusses about the usefulness of DEM and satellite digital data for extraction of structural features in SW of Zagros mountain, Iran.     

Modelling urban growth evolution and land-use changes using GIS based cellular automata and SLEUTH models: the case of Sana’a metropolitan city, Yemen

An effective and efficient planning of an urban growth and land use changes and its impact on the environment requires information about growth trends and patterns amongst other important information. Over the years, many urban growth models have been developed and used in the developed countries for forecasting growth patterns. In the developing countries however, there exist a very few studies showing the application of these models and their performances. In this study two models such as cellular automata (CA) and the SLEUTH models are applied in a geographical information system (GIS) to simulate and predict the urban growth and land use change for the City of Sana’a (Yemen) for the period 2004–2020. GIS based maps were generated for the urban growth pattern of the city which was further analyzed using geo-statistical techniques. During the models calibration process, a total of 35 years of time series dataset such as historical topographical maps, aerial photographs and satellite imageries was used to identify the parameters that influenced the urban growth. The validation result showed an overall accuracy of 99.6 %; with the producer’s accuracy of 83.3 % and the user’s accuracy 83.6 %. The SLEUTH model used the best fit growth rule parameters during the calibration to forecasting future urban growth pattern and generated various probability maps in which the individual grid cells are urbanized assuming unique “urban growth signatures”. The models generated future urban growth pattern and land use changes from the period 2004–2020. Both models proved effective in forecasting growth pattern that will be useful in planning and decision making. In comparison, the CA model growth pattern showed high density development, in which growth edges were filled and clusters were merged together to form a compact built-up area wherein less agricultural lands were included. On the contrary, the SLEUTH model growth pattern showed more urban sprawl and low-density development that included substantial areas of agricultural lands.     

A knowledge-driven GIS modeling technique for groundwater potential mapping at the Upper Langat Basin, Malaysia

The aim of this paper is to use a knowledge-driven expert-based geographical information system (GIS) model coupling with remote-sensing-derived parameters for groundwater potential mapping in an area of the Upper Langat Basin, Malaysia. In this study, nine groundwater storage controlling parameters that affect groundwater occurrences are derived from remotely sensed imagery, available maps, and associated databases. Those parameters are: lithology, slope, lineament, land use, soil, rainfall, drainage density, elevation, and geomorphology. Then the parameter layers were integrated and modeled using a knowledge-driven GIS of weighted linear combination. The weightage and score for each parameter and their classes are based on the Malaysian groundwater expert opinion survey. The predicted groundwater potential map was classified into four distinct zones based on the classification scheme designed by Department of Minerals and Geoscience Malaysia (JMG). The results showed that about 17% of the study area falls under low-potential zone, with 66% on moderate-potential zone, 15% with high-potential zone, and only 0.45% falls under very-high-potential zone. The results obtained in this study were validated with the groundwater borehole wells data compiled by the JMG and showed 76% of prediction accuracy. In addition statistical analysis indicated that hard rock dominant of the study area is controlled by secondary porosity such as distance from lineament and density of lineament. There are high correlations between area percentage of predicted groundwater potential zones and groundwater well yield. Results obtained from this study can be useful for future planning of groundwater exploration, planning and development by related agencies in Malaysia which provide a rapid method and reduce cost as well as less time consuming. The results may be also transferable to other areas of similar hydrological characteristics.     

A new class of LRS Bianchi type-II dark energy models with variable EoS parameter

A new class of dark energy models in a Locally Rotationally Symmetric Bianchi type-II (LRS B-II) space-time with variable equation of state (EoS) parameter and constant deceleration parameter have been investigated in the present paper. The Einstein’s field equations have been solved by applying a variation law for generalized Hubble’s parameter given by Berman: Nuovo Cimento 74:182 (1983) which generates two types of solutions for the average scale factor, one is of power-law type and other is of the exponential-law form. Using these two forms, Einstein’s field equations are solved separately that correspond to expanding singular and non-singular models of the universe respectively. The dark energy EoS parameter ω is found to be time dependent and its existing range for both models is in good agreement with the three recent observations of (i) SNe Ia data (Knop et al.: Astrophys. J. 598:102 (2003)), (ii) SNe Ia data collaborated with CMBR anisotropy and galaxy clustering statistics (Tegmark et al.: Astrophys. J. 606:702 (2004)) and latest (iii) a combination of cosmological datasets coming from CMB anisotropies, luminosity distances of high redshift type Ia supernovae and galaxy clustering (Hinshaw et al.: Astrophys. J. Suppl. 180:225 (2009); Komatsu et al. Astrophys. J. Suppl. 180:330 (2009)). The cosmological constant Λ is found to be a positive decreasing function of time and it approaches a small positive value at late time (i.e. the present epoch) which is corroborated by results from recent supernovae Ia observations. The physical and geometric behaviour of the universe have also been discussed in detail.